Questions and Answers
What is essential to start with for giant-scale services?
A professional data center and layer-7 switches
Which availability metric should be focused on as much as MTBF?
Data replication is sufficient for preserving uptime under faults.
False
What does intelligent admission control help implement?
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Use DQ analysis on all _____ to ensure reliability.
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What should be developed to minimize downtime during upgrades?
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Eric A. Brewer founded the Federal Search Foundation in 2001.
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Who is the Chief Scientist of Inktomi?
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What is one of Eric A. Brewer's research interests?
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What is one load-management approach that uses custom nodes for session management?
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Which approach includes clients in the load-management process?
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Round-robin DNS assigns different servers to different clients to achieve simple load balancing.
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What is the defined formula for yield?
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What does MTTR stand for?
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What is the preferred focus for giant-scale systems regarding availability?
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What happens to the effective size of a partitioned persistent store during a node failure?
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A perfect system would have 100 percent yield and 100 percent harvest.
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Which metric focuses on the fraction of completed queries?
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Which system aims to maintain 100 percent harvest under a fault?
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What are giant web services?
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The focus of the article is on wide-area issues such as network partitioning.
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Which of the following is NOT a component of the basic model for giant-scale services?
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What advantage does centralizing infrastructure services offer?
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What is the primary role of the load manager in giant-scale services?
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Giant-scale services should maintain _____ availability to meet user expectations.
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What is a significant challenge mentioned in relation to giant-scale services?
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What is the expected number of people with internet access predicted in the next ten years?
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In which type of traffic do read-only queries outnumber updates?
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Clusters in giant-scale services are used for independent faults.
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What load redirection method does the Inktomi search engine use?
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What is the implication of losing two of five nodes in a replica group?
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Replication on disk is cheap, but accessing the replicated data requires __________ points.
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Graceful degradation mechanisms are critical for delivering high availability.
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What can cause traffic to exceed average levels in online ticket sales?
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What basic constraints must be taken into account for graceful degradation?
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Which method guarantees that stock trade requests will be executed within 60 seconds?
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What is the role of dynamic database reduction?
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Natural disasters affect only one replica at a time.
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What is one approach to perform online evolution?
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During the 'big flip', we __________ switch all traffic to the upgraded nodes.
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Study Notes
Giant-Scale Services Overview
- Growth of web portals and ISPs, such as AOL and Yahoo, has multiplied over tenfold in five years.
- Essential focus on infrastructure services, which include instant messaging and various remote access applications.
Key Requirements of Giant-Scale Services
- Need for high availability, particularly for major platforms like eBay and CNN.
- Services must always be available, requiring robust infrastructure to handle growth and evolution.
Basic Model for Giant-Scale Services
- Services rely on a load manager to balance traffic among servers, enhancing availability.
- Clients access services over the internet, utilizing a best-effort IP network.
- Serves as an intermediary between external names and server IP addresses, ensuring reliability amidst server failures.
Advantages of the Basic Model
- Access Anywhere: Facilitates user access from various locations and devices, including set-top boxes and smart devices.
- Cost Efficiency: Centralized infrastructure allows for better resource utilization compared to standalone devices.
- Groupware Support: Centralizes data for collaboration tools, improving functionality for applications like teleconferencing and group management.
- Efficient Upgrades: Services can be upgraded seamlessly without physical distribution capabilities.
Clusters in Giant-Scale Services
- Clusters consist of multiple commodity servers functioning together to meet high scalability requirements.
- Example deployments:
- AOL Web cache: over 1,000 nodes, processing 10 billion queries/day.
- Inktomi search engine: over 1,000 nodes, more than 80 million queries/day.
- Nodes generally have a three-year depreciation timeline, providing scalability as service needs grow.
Load Management Advances
- Modern load management utilizes layer-4 and layer-7 switches to monitor server health and distribute traffic effectively.
- Methods include:
- Round-robin DNS for basic load balancing.
- Session management via service-specific front-end nodes.
Challenges and Considerations
- Downtime prevention is critical, requiring automatic detection and isolation of non-functioning nodes.
- Multiple load management strategies ensure service continuity and resilience during failures.
Persistent Data Store
- Data storage across servers uses replicated or partitioned approaches to maintain data availability and integrity.
- Includes options for network-attached storage systems to enhance overall system performance.
Implications for Design and Evolution
- Focus on scalability, availability, graceful degradation, and ease of upgrading is crucial for meeting user expectations.
- Equipment and operational costs are often weighed against the service bandwidth demands and service quality.### System Complexity and Design
- A simple Web farm utilizes round-robin DNS for load management with a persistent data store achieved by replicating all content to all nodes.
- Web farms typically experience no coherence traffic and may not require a backplane, although a secondary LAN for manual updates is common.
- In contrast, a search engine cluster supports external programs (e.g., Web servers) via layer-4 switches that balance load and mask faults, ensuring data availability despite node failures.
- Persistent data in search engine clusters is partitioned across servers, increasing capacity but risking data loss during server outages.
High Availability Metrics
- High availability is critical for large-scale systems; uptime, typically expressed in “nines,” measures how often a system is operational.
- Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR) are key components impacting uptime, with uptime calculable as:
uptime = (MTBF – MTTR) / MTBF. - Focus on improving MTTR is encouraged, as it allows for more manageable system adjustments compared to reducing MTBF.
Availability Measurement Terms
-
Yield: Fraction of completed queries, calculated as
yield = queries completed / queries offered. -
Harvest: Fraction of available data relative to the complete dataset, defined as
harvest = data available / complete data. - A perfect system would achieve 100% yield and 100% harvest, ensuring each query reflects the entire database.
DQ Principle
- The Data per Query (DQ) Principle suggests a relationship between data requested per query and queries processed per second, remaining constant under normal conditions.
- Design should accommodate capacity constraints tied to physical limitations like total I/O bandwidth, particularly at high utilization common in large-scale systems.
Replication vs. Partitioning
- Replication increases availability by maintaining complete data but reduces yield when failures occur; partitioning maintains yield better under fault conditions.
- Both methods initially maintain the same DQ value, but under failure, replication sees a yield reduction, while partitioned systems preserve yield but experience reduced harvest.
Capacity and Overload Considerations
- When a system experiences failures, redirected loads can drastically increase stress on remaining nodes, complicating server management.
- Replication is often deemed inefficient under high utilization without adequate excess capacity to handle failures, emphasizing the need for effective load-balancing solutions.
Graceful Degradation
- Mechanisms for graceful degradation become crucial during excess load conditions to maintain service availability.
- The DQ principle offers methods for graceful degradation, including limiting query capacity or reducing data to improve overall performance.
- Strategies may involve admissions controls to lessen query load or dynamic database reductions to lower the amount of data processed, thereby increasing the effective operational capacity.### Giant-Scale Services Overview
- Emphasis on graceful degradation to manage system availability during failures and saturation.
- Partitioned systems can replicate key data to enhance reliability, allowing a backup node to take over if the primary fails.
Cost-Based Admission Control (AC)
- Inktomi employs dynamic AC based on estimated query costs, balancing data and query metrics.
- Reducing data (D) during high demand allows for increased query capacity (Q), optimizing service provision.
- Simplistic policies may reduce D too aggressively, risking performance.
Priority-Based Admission Control
- Datek prioritizes stock trade queries, ensuring execution within a strict time frame, enhancing user experience.
- Low-value queries may be denied to preserve resources for higher-priority requests.
Data Freshness and Saturation
- Financial services may allow for less frequent updates on stock quotes during system saturation, trading off data accuracy for performance.
- Cached data may not reflect the current state, impacting user experience and system yield.
Disaster Tolerance Strategies
- Effective disaster recovery involves managing replica groups and implementing graceful degradation to mitigate failover impacts.
- Diversifying locations for replicas minimizes the risk from localized disasters.
Online Evolution and Migration
- Software and system upgrades are crucial for maintaining giant-scale services, with a focus on minimizing downtime and preserving quality.
- Upgrades can be executed through fast reboot, rolling upgrades, or "big flip," each impacting system availability differently.
Upgrade Approaches
- Fast Reboot: Quick system restart; dependent on staging areas to minimize service disruption.
- Rolling Upgrade: Sequential node upgrades; maintains service continuity with minimal capacity loss.
- Big Flip: Simultaneous upgrades of node halves; complex but effective for substantial changes, allowing for controlled failovers.
Key Lessons for Scalable Systems
- Establish a professional infrastructure with appropriate metrics for availability, focusing on both uptime and user experience.
- Monitor and measure performance with tools like DQ analysis to inform system operations and upgrades.
Automation and Control
- Maximize automation in upgrades to minimize disruptions, integrating smart clients for enhanced disaster recovery.
- Anticipate and plan for fault management through intelligent resource allocation and analysis.
Conclusion
- Understanding and managing availability metrics is critical in designing resilient giant-scale services.
- Continuously evolving systems require a balance between minimal changes and effective upgrades to maintain high performance.
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Description
Test your knowledge on the lessons learned from giant-scale web services. This quiz explores the new tools and methods required to handle issues in scalable internet services. Perfect for those interested in web technology and infrastructure.
